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TransMeme: A Multi-Agent Framework for Cross-Cultur... | AI Research

Key Takeaways

TransMeme is a multi-agent framework designed to adapt internet memes across different languages and cultures.
Internet memes are a pervasive form of multimodal online communication; however, such communication often involves users from diverse linguistic and cultural backgrounds.
Therefore, adapting memes across cultures and languages is a central challenge for enabling mutual understanding in online communication.
The framework strengthens target text adaptation with coordinated feedback to handle difficult cases that require deeper cultural or visual intervention.
We evaluate the framework on bidirectional Chinese-English meme transcreation using both human evaluation and LLM-as-a-Judge.

Paper AbstractExpand

Internet memes are a pervasive form of multimodal online communication; however, such communication often involves users from diverse linguistic and cultural backgrounds. Therefore, adapting memes across cultures and languages is a central challenge for enabling mutual understanding in online communication. Unlike ordinary translation or standalone text rewriting, cross-cultural meme transcreation must jointly preserve communicative intent, adapt culture-dependent meaning for the target audience, and maintain coherence between text and image. In this work, we first provide an explicit task analysis of cross-cultural meme transcreation and identify three core challenges: culture-specific knowledge understanding, intent and tone preservation, and multimodal consistency. Based on this analysis, we propose a multi-agent framework with specialized agents that are coordinated to address these challenges through cultural adaptation, target text rewriting, revision, and conditional visual adjustment. The framework strengthens target text adaptation with coordinated feedback to handle difficult cases that require deeper cultural or visual intervention. We evaluate the framework on bidirectional Chinese-English meme transcreation using both human evaluation and LLM-as-a-Judge. Our method consistently outperforms all baselines across both evaluation settings. In human evaluation, it achieves the best performance on all four dimensions and delivers a 33.1% average improvement over the strongest baseline, while in LLM-as-a-Judge, it attains the highest Top-1 ranking rate (60% versus 26% for the second-best baseline). Further analysis indicates that each component contributes to the performance. Our error analysis suggests that the remaining bottlenecks lie in humor reconstruction and image-text alignment rather than simple cultural knowledge gaps, pointing to future work on humor transfer.

TransMeme is a multi-agent framework designed to adapt internet memes across different languages and cultures. Unlike standard machine translation, which focuses on literal text, this framework aims to preserve the original communicative intent, humor, and social commentary of a meme while ensuring the final result is culturally relevant and visually coherent for a new audience.

The Challenges of Meme Transcreation

The authors identify three core obstacles that make meme adaptation difficult. First, memes rely on "culture-specific knowledge," such as local stereotypes or social references, which often fail to translate literally. Second, "intent and tone preservation" is required to maintain the original humor, sarcasm, or emotional stance. Finally, "multimodal consistency" is essential because a meme’s meaning is constructed through the interaction of text and image; changing the text without considering the visual template can break the meme's effectiveness.

How the Framework Works

The TransMeme system uses a multi-agent architecture to address these challenges through a structured pipeline:

Interpreter: Analyzes the source meme to identify its core intent, humor mechanism, and visual-textual dependencies.
Coordinator: Manages the workflow, routing complex cases that require deeper cultural or visual intervention to the full transcreation path.
Adapter: Creates a formal plan for cultural adaptation, deciding whether to keep, rephrase, or replace specific cultural references.
Rewriter: Generates target-language text using Direct Preference Optimization (DPO). This process is guided by the adaptation plan to ensure the text fits the intended tone and visual context.
Critic: Evaluates the intermediate output and provides feedback, triggering revisions if the text, culture, or visual elements are inconsistent.

Performance Results

The researchers evaluated TransMeme on 1,000 bidirectional Chinese-English meme instances. In human evaluations, the framework achieved an average score of 4.122, significantly outperforming the strongest baseline, which scored 3.097. In LLM-as-a-Judge testing, TransMeme was ranked as the top system 60% of the time, compared to 26% for the next best baseline. Component analysis showed that the DPO-enhanced rewriter and the critic-led revision loop were both critical to these performance gains.

Current Limitations

Despite the framework's success, the authors note that challenges remain. Error analysis indicates that the system still struggles with humor reconstruction and achieving perfect image-text alignment in certain contexts. These findings suggest that future research should focus on more advanced methods for humor transfer to further improve the quality of cross-cultural meme adaptation.

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